An advanced deep learning framework for skin cancer classification

Skin cancer detection: A review using deep learning techniques

Dildar, M., Akram, S., Irfan, M., Khan, H. U., Ramzan, M., Mahmood, A. R., Alsaiari, S. A., Saeed, A. H. M., Alraddadi, M. O., & Mahnashi, M. H. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph18105479 · Cited by 597 · see citing works

Skin lesion segmentation in dermoscopy images via deep full resolution convolutional networks

Al‐masni, M. A., Al–antari, M. A., Choi, M., Han, S., & Kim, T. (2018) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2018.05.027 · Cited by 484 · see citing works

The skin cancer classification using deep convolutional neural network

Dorj, U.-O., Lee, K.-K., Choi, J., & Lee, M. (2018) · Multimedia Tools and Applications · doi:10.1007/s11042-018-5714-1 · Cited by 406 · see citing works

Detection of Skin Cancer Based on Skin Lesion Images Using Deep Learning

Gouda, W., Sama, N. U., Al-Waakid, G., Humayun, M., & Jhanjhi, N. Z. (2022) · Healthcare · doi:10.3390/healthcare10071183 · Cited by 402 · see citing works

Skin Lesion Classification from Dermoscopic Images Using Deep Learning Techniques

Romero-López, A., Giró-i-Nieto, X., Burdick, J., & Marques, O. (2017) · Biomedical Engineering · doi:10.2316/p.2017.852-053 · Cited by 355 · see citing works

Deep learning techniques for skin lesion analysis and melanoma cancer detection: a survey of state-of-the-art

Adegun, A. A., & Viriri, S. (2020) · Artificial Intelligence Review · doi:10.1007/s10462-020-09865-y · Cited by 331 · see citing works

A multi-class skin Cancer classification using deep convolutional neural networks

Chaturvedi, S. S., Tembhurne, J. V., & Diwan, T. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-09388-2 · Cited by 294 · see citing works

The Development of a Skin Cancer Classification System for Pigmented Skin Lesions Using Deep Learning

Jinnai, S., Yamazaki, N., Hirano, Y., Sugawara, Y., Ohe, Y., & Hamamoto, R. (2020) · Biomolecules · doi:10.3390/biom10081123 · Cited by 276 · see citing works

Region-of-Interest Based Transfer Learning Assisted Framework for Skin Cancer Detection

Ashraf, R., Afzal, S., Rehman, A. U., Gul, S., Baber, J., Bakhtyar, M., Mehmood, I., Song, O.-Y., & Maqsood, M. (2020) · IEEE Access · doi:10.1109/access.2020.3014701 · Cited by 261 · see citing works

Skin cancer detection: Applying a deep learning based model driven architecture in the cloud for classifying dermal cell images

Kadampur, M. A., & Alriyaee, S. (2019) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2019.100282 · Cited by 257 · see citing works

A comparative study of deep learning architectures on melanoma detection

Kassani, S. H., & Kassani, P. H. (2019) · Tissue and Cell · doi:10.1016/j.tice.2019.04.009 · Cited by 254 · see citing works

Diabetic Retinopathy Detection Using VGG-NIN a Deep Learning Architecture

Khan, Z., Khan, F. G., Khan, A., et al. (2021) · IEEE Access · doi:10.1109/access.2021.3074422 · Cited by 252 · see citing works

Uncertainty quantification in skin cancer classification using three-way decision-based Bayesian deep learning

Abdar, M., Samami, M., Mahmoodabad, S. D., Doan, T., Mazoure, B., Hashemifesharaki, R., Liu, L., Khosravi, A., Acharya, U. R., Makarenkov, V., et al. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104418 · Cited by 241 · see citing works

Deep Learning-Based System for Automatic Melanoma Detection

Adegun, A. A., & Viriri, S. (2019) · IEEE Access · doi:10.1109/access.2019.2962812 · Cited by 232 · see citing works

Melanoma skin cancer detection using deep learning and classical machine learning techniques: A hybrid approach

Daghrir, J., Tlig, L., Bouchouicha, M., et al. (2020) · International Conference on Advanced Technologies for Signal and Image Processing · doi:10.1109/atsip49331.2020.9231544 · Cited by 189 · see citing works

Skin cancer detection using ensemble of machine learning and deep learning techniques

Tembhurne, J. V., Hebbar, N., Patil, H. Y., & Diwan, T. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-14697-3 · Cited by 185 · see citing works

Machine learning and deep learning algorithms for skin cancer classification from dermoscopic images

Bechelli, S., & Delhommelle, J. (2022) · Bioengineering · doi:10.3390/bioengineering9030097 · Cited by 181 · see citing works

Melanoma and Nevus Skin Lesion Classification Using Handcraft and Deep Learning Feature Fusion via Mutual Information Measures

Almaraz-Damian, J. A., Ponomaryov, V. I., Sadovnychiy, S., & Castillejos, H. (2020) · Entropy · doi:10.3390/e22040484 · Cited by 179 · see citing works

An internet of health things‐driven deep learning framework for detection and classification of skin cancer using transfer learning

Khamparia, A., Singh, P. K., Rani, P., Samanta, D., Khanna, A. K., & Bhushan, B. (2020) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.3963 · Cited by 177 · see citing works

Skin Cancer Detection Using Combined Decision of Deep Learners

Imran, A., Nasir, A., Bilal, M., Sun, G., Alzahrani, A., & Almuhaimeed, A. (2022) · IEEE Access · doi:10.1109/access.2022.3220329 · Cited by 166 · see citing works

Machine Learning and Its Application in Skin Cancer

Das, K., Cockerell, C. J., Patil, A. D., Pietkiewicz, P., Giulini, M., Grabbe, S., & Goldust, M. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph182413409 · Cited by 163 · see citing works

An explainable stacked ensemble of deep learning models for improved melanoma skin cancer detection

Shorfuzzaman, M. (2021) · Multimedia Systems · doi:10.1007/s00530-021-00787-5 · Cited by 162 · see citing works

InSiNet: a deep convolutional approach to skin cancer detection and segmentation

Reis, H. Ç., Turk, V., Khoshelham, K., & Kaya, S. (2022) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-021-02473-0 · Cited by 161 · see citing works

Automatic Malignant and Benign Skin Cancer Classification Using a Hybrid Deep Learning Approach

Bassel, A., Abdulkareem, A. B., Alyasseri, Z. A. A., Sani, N. S., & Mohammed, H. J. (2022) · Diagnostics · doi:10.3390/diagnostics12102472 · Cited by 160 · see citing works

A Convolutional Neural Network Framework for Accurate Skin Cancer Detection

Thurnhofer-Hemsi, K., & Domínguez, E. (2021) · Neural Processing Letters · doi:10.1007/s11063-020-10364-y · Cited by 157 · see citing works

Skin cancer disease images classification using deep learning solutions

Mijwil, M. M. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-021-10952-7 · Cited by 154 · see citing works

Deep Learning Methods for Accurate Skin Cancer Recognition and Mobile Application

Kousis, I., Perikos, I., Hatzilygeroudis, I., & Virvou, M. K. (2022) · Electronics · doi:10.3390/electronics11091294 · Cited by 148 · see citing works

The Role of Machine Learning and Deep Learning Approaches for the Detection of Skin Cancer

Mazhar, T., Haq, I., Ditta, A., Mohsan, S. A. H., Rehman, F., Zafar, I., Gansau, J. A., & Goh, L. P. W. (2023) · Healthcare · doi:10.3390/healthcare11030415 · Cited by 131 · see citing works

An Improved VGG Model for Skin Cancer Detection

Tabrizchi, H., Parvizpour, S., & Razmara, J. (2022) · Neural Processing Letters · doi:10.1007/s11063-022-10927-1 · Cited by 104 · see citing works

Automatic Classification of Melanoma Skin Cancer with Deep Convolutional Neural Networks

Aljohani, K., & Turki, T. (2022) · AI · doi:10.3390/ai3020029 · Cited by 99 · see citing works

Melanoma Diagnosis Using Deep Learning and Fuzzy Logic

Banerjee, S., Singh, S. K., Chakraborty, A., Das, A., & Bag, R. (2020) · Diagnostics · doi:10.3390/diagnostics10080577 · Cited by 96 · see citing works

Classification of Skin cancer using deep learning, Convolutional Neural Networks - Opportunities and vulnerabilities- A systematic Review

Manne, R. (2020) · International Journal for Modern Trends in Science and Technology · doi:10.46501/ijmtst061118 · Cited by 95 · see citing works

Deep Semantic Segmentation and Multi-Class Skin Lesion Classification Based on Convolutional Neural Network

Anjum, M. A., Amin, J., Sharif, M. I., Khan, H. U., Malik, M. S. A., & Kadry, S. (2020) · IEEE Access · doi:10.1109/access.2020.3009276 · Cited by 81 · see citing works

Multiclass Skin Cancer Classification Using Ensemble of Fine-Tuned Deep Learning Models

Kausar, N., Hameed, A., Sattar, M., Ashraf, R., Imran, A. S., Abidin, M. Z. U., & Ali, A. (2021) · Applied Sciences · doi:10.3390/app112210593 · Cited by 81 · see citing works

Uncovering and Correcting Shortcut Learning in Machine Learning Models for Skin Cancer Diagnosis

Nauta, M., Walsh, R., Dubowski, A., & Seifert, C. (2021) · Diagnostics · doi:10.3390/diagnostics12010040 · Cited by 67 · see citing works

Remote Diagnosis and Triaging Model for Skin Cancer Using EfficientNet and Extreme Gradient Boosting

Khan, I. U., Aslam, N., Anwar, T., Aljameel, S. S., Ullah, M., Khan, R., Rehman, A., & Akhtar, N. (2021) · Complexity · doi:10.1155/2021/5591614 · Cited by 63 · see citing works

Diagnosis of melanoma from dermoscopic images using a deep depthwise separable residual convolutional network

Sarkar, R., Chatterjee, C. C., & Hazra, A. (2019) · IET Image Processing · doi:10.1049/iet-ipr.2018.6669 · Cited by 54 · see citing works

Comparison of machine learning strategies for infrared thermography of skin cancer

Magalhaes, C., Tavares, J. M. R. S., Mendes, J. G., & Vardasca, R. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.102872 · Cited by 54 · see citing works

Deep learning enhances polarization speckle for in vivo skin cancer detection

Wang, Y., Louie, D. C., Cai, J., Tchvialeva, L., Lui, H., Wang, Z. J., & Lee, T. K. (2021) · Optics & Laser Technology · doi:10.1016/j.optlastec.2021.107006 · Cited by 52 · see citing works

Interpretable Deep Convolutional Neural Networks via Meta-learning

Liu, X., Wang, X., & Matwin, S. (2018) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2018.8489172 · Cited by 51 · see citing works

Transfer Learning Based Method for Two-Step Skin Cancer Images Classification

Moldovan, D. (2019) · E-Health and Bioengineering Conference (EHB) · doi:10.1109/ehb47216.2019.8970067 · Cited by 51 · see citing works

Extracting morphological high-level intuitive features (HLIF) for enhancing skin lesion classification

Amelard, R., Wong, A. K. C., & Clausi, D. A. (2012) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2012.6346956 · Cited by 50 · see citing works

Dense deconvolution net: Multi path fusion and dense deconvolution for high resolution skin lesion segmentation

He, X., Yu, Z. X., Wang, T., et al. (2018) · Technology and Health Care · doi:10.3233/thc-174633 · Cited by 36 · see citing works

Automatic Diagnosis of Skin Cancer Using Neural Networks

Jianu, S.-R.-S., Ichim, L., & Popescu, D. (2019) · International Symposium on Advanced Topics in Electrical Engineering · doi:10.1109/atee.2019.8724938 · Cited by 36 · see citing works

Lesion Segmentation and Automated Melanoma Detection using Deep Convolutional Neural Networks and XGBoost

Pham, H. N., Koay, C. Y., Chakraborty, T., et al. (2019) · International Conference on System Science and Engineering · doi:10.1109/icsse.2019.8823129 · Cited by 32 · see citing works

Melanoma Skin Cancer Detection Using Deep Learning and Advanced Regularizer

Hossin, M. A., Rupom, F. F., Mahi, H. R., et al. (2020) · International Conference on Advanced Computer Science and Information Systems · doi:10.1109/icacsis51025.2020.9263118 · Cited by 30 · see citing works

Multi-class Semantic Segmentation of Skin Lesions via Fully Convolutional Networks

Goyal, M., Yap, M. H., & Hassanpour, S. (2020) · International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC) · doi:10.5220/0009380302900295 · Cited by 17 · see citing works

Deep Learning-based Transfer Learning for Classification of Skin Cancer

Kalaiarasan, R., Madhan Kumar, K., Sridhar, S., et al. (2022) · International Conference on Applied Artificial Intelligence and Computing (ICAAIC) · doi:10.1109/icaaic53929.2022.9792651 · Cited by 12 · see citing works

Interpretable multimodal fusion for skin lesion classification using dermoscopic images and patient metadata

Sonuç, E., Saihood, Q., Yargı Baydilli, Y., & Özcan, E. (2026) · Frontiers in Medicine · doi:10.3389/fmed.2026.1871689

Overview of deep learning in medical imaging

Suzuki, K. (2017) · Radiological Physics and Technology · doi:10.1007/s12194-017-0406-5 · Cited by 1,102 · see citing works

Survey of Machine Learning Applications of Convolutional Neural Networks to Medical Image Analysis

Kumar, K. N. (2021) · International Journal for Research in Applied Science and Engineering Technology · doi:10.22214/ijraset.2021.38947 · Cited by 2 · see citing works

Exploring Deep Learning for View-Based 3D Model Retrieval

Gao, Z., Li, Y., & Wan, S. (2020) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3377876 · Cited by 108 · see citing works

A Comprehensive Analysis of Machine Learning Techniques in Biomedical Image Processing Using Convolutional Neural Network

Shrivastava, A., Chakkaravathy, M., & Shah, M. A. (2022) · International Conference on Contemporary Computing and Informatics (IC3I) · doi:10.1109/ic3i56241.2022.10072911 · Cited by 27 · see citing works

Deep learning ensemble 2D CNN approach towards the detection of lung cancer

Shah, A. A., Malik, H. A. M., Muhammad, A., Alourani, A., & Butt, Z. A. (2023) · Scientific Reports · doi:10.1038/s41598-023-29656-z · Cited by 211 · see citing works